Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.1 KiB
Average record size in memory120.4 B

Variable types

Numeric14
Categorical1

Alerts

ID is highly overall correlated with rad and 1 other fieldsHigh correlation
age is highly overall correlated with crim and 7 other fieldsHigh correlation
crim is highly overall correlated with age and 8 other fieldsHigh correlation
dis is highly overall correlated with age and 6 other fieldsHigh correlation
indus is highly overall correlated with age and 7 other fieldsHigh correlation
lstat is highly overall correlated with age and 7 other fieldsHigh correlation
medv is highly overall correlated with age and 7 other fieldsHigh correlation
nox is highly overall correlated with age and 8 other fieldsHigh correlation
ptratio is highly overall correlated with medvHigh correlation
rad is highly overall correlated with ID and 3 other fieldsHigh correlation
rm is highly overall correlated with lstat and 1 other fieldsHigh correlation
tax is highly overall correlated with ID and 8 other fieldsHigh correlation
zn is highly overall correlated with age and 4 other fieldsHigh correlation
chas is highly imbalanced (67.2%)Imbalance
ID has unique valuesUnique
zn has 248 (74.5%) zerosZeros

Reproduction

Analysis started2024-08-19 18:25:02.311310
Analysis finished2024-08-19 18:25:29.122635
Duration26.81 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct333
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.95195
Minimum1
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:29.232050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.4
Q1123
median244
Q3377
95-th percentile480.4
Maximum506
Range505
Interquartile range (IQR)254

Descriptive statistics

Standard deviation147.85944
Coefficient of variation (CV)0.58919421
Kurtosis-1.2319882
Mean250.95195
Median Absolute Deviation (MAD)127
Skewness0.06411292
Sum83567
Variance21862.413
MonotonicityStrictly increasing
2024-08-19T20:25:29.432746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
345 1
 
0.3%
343 1
 
0.3%
342 1
 
0.3%
341 1
 
0.3%
340 1
 
0.3%
339 1
 
0.3%
337 1
 
0.3%
335 1
 
0.3%
334 1
 
0.3%
Other values (323) 323
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
4 1
0.3%
5 1
0.3%
7 1
0.3%
11 1
0.3%
12 1
0.3%
13 1
0.3%
14 1
0.3%
15 1
0.3%
ValueCountFrequency (%)
506 1
0.3%
504 1
0.3%
503 1
0.3%
502 1
0.3%
500 1
0.3%
498 1
0.3%
494 1
0.3%
493 1
0.3%
492 1
0.3%
491 1
0.3%

crim
Real number (ℝ)

HIGH CORRELATION 

Distinct332
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3603415
Minimum0.00632
Maximum73.5341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:29.621466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.023736
Q10.07896
median0.26169
Q33.67822
95-th percentile15.22152
Maximum73.5341
Range73.52778
Interquartile range (IQR)3.59926

Descriptive statistics

Standard deviation7.3522718
Coefficient of variation (CV)2.1879538
Kurtosis30.924065
Mean3.3603415
Median Absolute Deviation (MAD)0.2312
Skewness4.5989812
Sum1118.9937
Variance54.055901
MonotonicityNot monotonic
2024-08-19T20:25:29.814460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01501 2
 
0.6%
0.00632 1
 
0.3%
0.04544 1
 
0.3%
0.01301 1
 
0.3%
0.06151 1
 
0.3%
0.05497 1
 
0.3%
0.03306 1
 
0.3%
0.03427 1
 
0.3%
0.03738 1
 
0.3%
0.05083 1
 
0.3%
Other values (322) 322
96.7%
ValueCountFrequency (%)
0.00632 1
0.3%
0.00906 1
0.3%
0.01096 1
0.3%
0.01301 1
0.3%
0.01311 1
0.3%
0.0136 1
0.3%
0.01432 1
0.3%
0.01439 1
0.3%
0.01501 2
0.6%
0.01778 1
0.3%
ValueCountFrequency (%)
73.5341 1
0.3%
45.7461 1
0.3%
38.3518 1
0.3%
37.6619 1
0.3%
28.6558 1
0.3%
25.9406 1
0.3%
25.0461 1
0.3%
24.8017 1
0.3%
24.3938 1
0.3%
22.5971 1
0.3%

zn
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.689189
Minimum0
Maximum100
Zeros248
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:30.000365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile77
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation22.674762
Coefficient of variation (CV)2.1212799
Kurtosis4.8793739
Mean10.689189
Median Absolute Deviation (MAD)0
Skewness2.3740517
Sum3559.5
Variance514.14482
MonotonicityNot monotonic
2024-08-19T20:25:30.170070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 248
74.5%
20 14
 
4.2%
80 7
 
2.1%
25 7
 
2.1%
22 7
 
2.1%
12.5 6
 
1.8%
45 5
 
1.5%
33 3
 
0.9%
34 3
 
0.9%
55 3
 
0.9%
Other values (15) 30
 
9.0%
ValueCountFrequency (%)
0 248
74.5%
12.5 6
 
1.8%
17.5 1
 
0.3%
18 1
 
0.3%
20 14
 
4.2%
21 3
 
0.9%
22 7
 
2.1%
25 7
 
2.1%
28 2
 
0.6%
30 3
 
0.9%
ValueCountFrequency (%)
100 1
 
0.3%
95 3
0.9%
90 3
0.9%
85 2
 
0.6%
82.5 1
 
0.3%
80 7
2.1%
75 3
0.9%
60 3
0.9%
55 3
0.9%
52.5 1
 
0.3%

indus
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.293483
Minimum0.74
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:30.351201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile2.18
Q15.13
median9.9
Q318.1
95-th percentile21.89
Maximum27.74
Range27
Interquartile range (IQR)12.97

Descriptive statistics

Standard deviation6.9981231
Coefficient of variation (CV)0.61966028
Kurtosis-1.2402092
Mean11.293483
Median Absolute Deviation (MAD)6.57
Skewness0.29043376
Sum3760.73
Variance48.973727
MonotonicityNot monotonic
2024-08-19T20:25:30.547572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 88
26.4%
19.58 21
 
6.3%
6.2 14
 
4.2%
8.14 13
 
3.9%
21.89 10
 
3.0%
9.9 9
 
2.7%
8.56 8
 
2.4%
3.97 8
 
2.4%
6.91 7
 
2.1%
5.86 7
 
2.1%
Other values (58) 148
44.4%
ValueCountFrequency (%)
0.74 1
0.3%
1.21 1
0.3%
1.22 1
0.3%
1.25 1
0.3%
1.32 1
0.3%
1.38 1
0.3%
1.47 1
0.3%
1.52 2
0.6%
1.69 1
0.3%
1.76 1
0.3%
ValueCountFrequency (%)
27.74 4
 
1.2%
25.65 6
 
1.8%
21.89 10
 
3.0%
19.58 21
 
6.3%
18.1 88
26.4%
15.04 2
 
0.6%
13.92 3
 
0.9%
13.89 1
 
0.3%
12.83 4
 
1.2%
11.93 4
 
1.2%

chas
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
313 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 313
94.0%
1 20
 
6.0%

Length

2024-08-19T20:25:30.721869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-19T20:25:30.850379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 313
94.0%
1 20
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 313
94.0%
1 20
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 313
94.0%
1 20
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 313
94.0%
1 20
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 313
94.0%
1 20
 
6.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55714414
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:31.047909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.411
Q10.453
median0.538
Q30.631
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.178

Descriptive statistics

Standard deviation0.11495451
Coefficient of variation (CV)0.20632813
Kurtosis-0.1040713
Mean0.55714414
Median Absolute Deviation (MAD)0.086
Skewness0.70555166
Sum185.529
Variance0.013214539
MonotonicityNot monotonic
2024-08-19T20:25:31.240233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538 14
 
4.2%
0.713 14
 
4.2%
0.437 12
 
3.6%
0.605 11
 
3.3%
0.871 10
 
3.0%
0.624 10
 
3.0%
0.544 9
 
2.7%
0.489 9
 
2.7%
0.74 9
 
2.7%
0.7 8
 
2.4%
Other values (67) 227
68.2%
ValueCountFrequency (%)
0.385 1
 
0.3%
0.389 1
 
0.3%
0.398 2
0.6%
0.4 1
 
0.3%
0.401 3
0.9%
0.403 2
0.6%
0.404 1
 
0.3%
0.405 1
 
0.3%
0.409 2
0.6%
0.41 2
0.6%
ValueCountFrequency (%)
0.871 10
3.0%
0.77 6
1.8%
0.74 9
2.7%
0.718 3
 
0.9%
0.713 14
4.2%
0.7 8
2.4%
0.693 7
2.1%
0.679 7
2.1%
0.671 2
 
0.6%
0.668 3
 
0.9%

rm
Real number (ℝ)

HIGH CORRELATION 

Distinct308
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2656186
Minimum3.561
Maximum8.725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:31.434770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.2376
Q15.884
median6.202
Q36.595
95-th percentile7.5014
Maximum8.725
Range5.164
Interquartile range (IQR)0.711

Descriptive statistics

Standard deviation0.70395158
Coefficient of variation (CV)0.11235149
Kurtosis2.0517526
Mean6.2656186
Median Absolute Deviation (MAD)0.333
Skewness0.28402752
Sum2086.451
Variance0.49554782
MonotonicityNot monotonic
2024-08-19T20:25:31.628459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.229 3
 
0.9%
6.127 3
 
0.9%
6.417 2
 
0.6%
6.405 2
 
0.6%
5.854 2
 
0.6%
6.144 2
 
0.6%
6.03 2
 
0.6%
5.304 2
 
0.6%
5.888 2
 
0.6%
5.713 2
 
0.6%
Other values (298) 311
93.4%
ValueCountFrequency (%)
3.561 1
0.3%
3.863 1
0.3%
4.138 1
0.3%
4.368 1
0.3%
4.519 1
0.3%
4.652 1
0.3%
4.906 1
0.3%
4.926 1
0.3%
4.963 1
0.3%
4.97 1
0.3%
ValueCountFrequency (%)
8.725 1
0.3%
8.398 1
0.3%
8.375 1
0.3%
8.337 1
0.3%
8.266 1
0.3%
8.259 1
0.3%
8.247 1
0.3%
8.04 1
0.3%
8.034 1
0.3%
7.929 1
0.3%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct260
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.226426
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:31.824549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17.62
Q145.4
median76.7
Q393.8
95-th percentile100
Maximum100
Range94
Interquartile range (IQR)48.4

Descriptive statistics

Standard deviation28.133344
Coefficient of variation (CV)0.41235259
Kurtosis-0.93608546
Mean68.226426
Median Absolute Deviation (MAD)20
Skewness-0.60464361
Sum22719.4
Variance791.48502
MonotonicityNot monotonic
2024-08-19T20:25:32.034135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 26
 
7.8%
96 4
 
1.2%
76.5 3
 
0.9%
97.3 3
 
0.9%
21.4 3
 
0.9%
95.4 3
 
0.9%
98.8 3
 
0.9%
95.6 3
 
0.9%
84.1 2
 
0.6%
97 2
 
0.6%
Other values (250) 281
84.4%
ValueCountFrequency (%)
6 1
0.3%
6.2 1
0.3%
6.5 1
0.3%
6.6 2
0.6%
7.8 2
0.6%
8.4 1
0.3%
8.9 1
0.3%
9.8 1
0.3%
9.9 1
0.3%
13 1
0.3%
ValueCountFrequency (%)
100 26
7.8%
99.3 1
 
0.3%
98.9 1
 
0.3%
98.8 3
 
0.9%
98.7 1
 
0.3%
98.5 1
 
0.3%
98.4 2
 
0.6%
98.2 2
 
0.6%
98.1 1
 
0.3%
98 1
 
0.3%

dis
Real number (ℝ)

HIGH CORRELATION 

Distinct295
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7099336
Minimum1.1296
Maximum10.7103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:32.405143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.5022
Q12.1224
median3.0923
Q35.1167
95-th percentile7.49938
Maximum10.7103
Range9.5807
Interquartile range (IQR)2.9943

Descriptive statistics

Standard deviation1.9811231
Coefficient of variation (CV)0.53400498
Kurtosis0.11355108
Mean3.7099336
Median Absolute Deviation (MAD)1.1622
Skewness0.93814296
Sum1235.4079
Variance3.9248485
MonotonicityNot monotonic
2024-08-19T20:25:32.668694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.4007 3
 
0.9%
4.8122 3
 
0.9%
3.6519 3
 
0.9%
5.2873 3
 
0.9%
6.8147 3
 
0.9%
5.4917 3
 
0.9%
5.7209 3
 
0.9%
6.4798 3
 
0.9%
5.118 2
 
0.6%
3.9454 2
 
0.6%
Other values (285) 305
91.6%
ValueCountFrequency (%)
1.1296 1
0.3%
1.137 1
0.3%
1.1691 1
0.3%
1.1742 1
0.3%
1.2024 1
0.3%
1.2852 1
0.3%
1.3216 1
0.3%
1.3325 1
0.3%
1.3449 1
0.3%
1.358 1
0.3%
ValueCountFrequency (%)
10.7103 1
0.3%
9.2229 1
0.3%
9.1876 1
0.3%
9.0892 1
0.3%
8.9067 1
0.3%
8.7921 1
0.3%
8.6966 1
0.3%
8.5353 1
0.3%
8.344 1
0.3%
8.3248 1
0.3%

rad
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6336336
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:32.834354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7421743
Coefficient of variation (CV)0.90746386
Kurtosis-0.90867887
Mean9.6336336
Median Absolute Deviation (MAD)2
Skewness0.98325763
Sum3208
Variance76.425612
MonotonicityNot monotonic
2024-08-19T20:25:33.291081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 88
26.4%
5 76
22.8%
4 70
21.0%
3 27
 
8.1%
8 18
 
5.4%
1 14
 
4.2%
2 14
 
4.2%
6 13
 
3.9%
7 13
 
3.9%
ValueCountFrequency (%)
1 14
 
4.2%
2 14
 
4.2%
3 27
 
8.1%
4 70
21.0%
5 76
22.8%
6 13
 
3.9%
7 13
 
3.9%
8 18
 
5.4%
24 88
26.4%
ValueCountFrequency (%)
24 88
26.4%
8 18
 
5.4%
7 13
 
3.9%
6 13
 
3.9%
5 76
22.8%
4 70
21.0%
3 27
 
8.1%
2 14
 
4.2%
1 14
 
4.2%

tax
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409.27928
Minimum188
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:33.469794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile219.6
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range523
Interquartile range (IQR)387

Descriptive statistics

Standard deviation170.84199
Coefficient of variation (CV)0.41742154
Kurtosis-1.1907888
Mean409.27928
Median Absolute Deviation (MAD)78
Skewness0.63302676
Sum136290
Variance29186.985
MonotonicityNot monotonic
2024-08-19T20:25:33.666855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 88
26.4%
307 27
 
8.1%
403 21
 
6.3%
437 10
 
3.0%
398 9
 
2.7%
304 9
 
2.7%
296 8
 
2.4%
224 8
 
2.4%
264 8
 
2.4%
384 8
 
2.4%
Other values (49) 137
41.1%
ValueCountFrequency (%)
188 6
1.8%
193 6
1.8%
198 1
 
0.3%
216 4
1.2%
222 5
1.5%
223 3
 
0.9%
224 8
2.4%
226 1
 
0.3%
233 7
2.1%
241 1
 
0.3%
ValueCountFrequency (%)
711 4
 
1.2%
666 88
26.4%
469 1
 
0.3%
437 10
 
3.0%
432 6
 
1.8%
430 2
 
0.6%
422 1
 
0.3%
411 1
 
0.3%
403 21
 
6.3%
402 1
 
0.3%

ptratio
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.448048
Minimum12.6
Maximum21.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:33.852830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19
Q320.2
95-th percentile21
Maximum21.2
Range8.6
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1518213
Coefficient of variation (CV)0.11664222
Kurtosis-0.35255041
Mean18.448048
Median Absolute Deviation (MAD)1.2
Skewness-0.78983814
Sum6143.2
Variance4.630335
MonotonicityNot monotonic
2024-08-19T20:25:34.027921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20.2 94
28.2%
14.7 24
 
7.2%
21 17
 
5.1%
17.8 16
 
4.8%
17.4 14
 
4.2%
19.1 13
 
3.9%
18.4 12
 
3.6%
16.6 11
 
3.3%
18.6 10
 
3.0%
21.2 10
 
3.0%
Other values (32) 112
33.6%
ValueCountFrequency (%)
12.6 1
 
0.3%
13 8
 
2.4%
13.6 1
 
0.3%
14.7 24
7.2%
14.9 3
 
0.9%
15.1 1
 
0.3%
15.2 9
 
2.7%
15.3 3
 
0.9%
15.5 1
 
0.3%
15.6 2
 
0.6%
ValueCountFrequency (%)
21.2 10
 
3.0%
21.1 1
 
0.3%
21 17
 
5.1%
20.9 8
 
2.4%
20.2 94
28.2%
20.1 4
 
1.2%
19.7 5
 
1.5%
19.6 4
 
1.2%
19.2 9
 
2.7%
19.1 13
 
3.9%

black
Real number (ℝ)

Distinct237
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.4661
Minimum3.5
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:34.258144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile97.462
Q1376.73
median392.05
Q3396.24
95-th percentile396.9
Maximum396.9
Range393.4
Interquartile range (IQR)19.51

Descriptive statistics

Standard deviation86.584567
Coefficient of variation (CV)0.24086991
Kurtosis8.0184665
Mean359.4661
Median Absolute Deviation (MAD)4.85
Skewness-2.9984217
Sum119702.21
Variance7496.8872
MonotonicityNot monotonic
2024-08-19T20:25:34.508786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 79
 
23.7%
395.24 3
 
0.9%
395.63 2
 
0.6%
377.07 2
 
0.6%
392.8 2
 
0.6%
394.72 2
 
0.6%
393.37 2
 
0.6%
389.71 2
 
0.6%
393.68 2
 
0.6%
393.23 2
 
0.6%
Other values (227) 235
70.6%
ValueCountFrequency (%)
3.5 1
0.3%
3.65 1
0.3%
7.68 1
0.3%
9.32 1
0.3%
16.45 1
0.3%
18.82 1
0.3%
22.01 1
0.3%
27.25 1
0.3%
43.06 1
0.3%
48.45 1
0.3%
ValueCountFrequency (%)
396.9 79
23.7%
396.42 1
 
0.3%
396.33 1
 
0.3%
396.3 1
 
0.3%
396.28 1
 
0.3%
396.24 1
 
0.3%
396.21 2
 
0.6%
396.14 1
 
0.3%
396.06 1
 
0.3%
395.99 1
 
0.3%

lstat
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.515435
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:34.726558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.656
Q17.18
median10.97
Q316.42
95-th percentile26.692
Maximum37.97
Range36.24
Interquartile range (IQR)9.24

Descriptive statistics

Standard deviation7.0677808
Coefficient of variation (CV)0.56472512
Kurtosis0.74869625
Mean12.515435
Median Absolute Deviation (MAD)4.41
Skewness0.97832756
Sum4167.64
Variance49.953525
MonotonicityNot monotonic
2024-08-19T20:25:34.937461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.13 3
 
0.9%
5.68 2
 
0.6%
6.36 2
 
0.6%
5.5 2
 
0.6%
7.39 2
 
0.6%
9.97 2
 
0.6%
23.98 2
 
0.6%
7.79 2
 
0.6%
14.1 2
 
0.6%
3.95 2
 
0.6%
Other values (300) 312
93.7%
ValueCountFrequency (%)
1.73 1
0.3%
1.98 1
0.3%
2.47 1
0.3%
2.87 1
0.3%
2.88 1
0.3%
2.94 1
0.3%
2.96 1
0.3%
3.01 1
0.3%
3.13 1
0.3%
3.16 2
0.6%
ValueCountFrequency (%)
37.97 1
0.3%
36.98 1
0.3%
34.77 1
0.3%
34.41 1
0.3%
31.99 1
0.3%
30.63 1
0.3%
30.59 1
0.3%
29.68 1
0.3%
29.55 1
0.3%
29.53 1
0.3%

medv
Real number (ℝ)

HIGH CORRELATION 

Distinct192
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.768769
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-08-19T20:25:35.166195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.5
Q117.4
median21.6
Q325
95-th percentile43.26
Maximum50
Range45
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation9.173468
Coefficient of variation (CV)0.40289697
Kurtosis1.558037
Mean22.768769
Median Absolute Deviation (MAD)3.9
Skewness1.1224717
Sum7582
Variance84.152516
MonotonicityNot monotonic
2024-08-19T20:25:35.364197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 11
 
3.3%
20.6 5
 
1.5%
19.4 5
 
1.5%
17.8 5
 
1.5%
25 5
 
1.5%
22 5
 
1.5%
23.1 5
 
1.5%
23.9 5
 
1.5%
19.3 5
 
1.5%
21.4 4
 
1.2%
Other values (182) 278
83.5%
ValueCountFrequency (%)
5 1
0.3%
5.6 1
0.3%
7 1
0.3%
7.2 2
0.6%
7.4 1
0.3%
8.1 1
0.3%
8.3 2
0.6%
8.4 1
0.3%
8.7 1
0.3%
8.8 2
0.6%
ValueCountFrequency (%)
50 11
3.3%
48.8 1
 
0.3%
48.5 1
 
0.3%
48.3 1
 
0.3%
46 1
 
0.3%
44.8 1
 
0.3%
43.5 1
 
0.3%
43.1 1
 
0.3%
42.8 1
 
0.3%
41.7 1
 
0.3%

Interactions

2024-08-19T20:25:27.035420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:02.743045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:04.454300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.845223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.486575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.299880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.084573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.705193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:15.460125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.321927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.973466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.778893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.903467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.271972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.147231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:02.863483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.130001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.960816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.601800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.442222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.202342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.821731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:15.577673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.444206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.089292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.950506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:23.035781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.401346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.271838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:02.991213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.257349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.095616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.726075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.568721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.327154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.955886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:15.945825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.567565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.216696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:21.081112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:23.179389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.546754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.391192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.108074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.384542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.209048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.839291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.689428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.440190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.070378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.061564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.686834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.336431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:21.212370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:23.312172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.672805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.505723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.225680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.516522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.329063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.956636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.818411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.555432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.192536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.175030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.805684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.454549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:21.338713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:23.445674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.798164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.619039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.343088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.661901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.441578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.071702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.926106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.667115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.307486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.283353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.922627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.567546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:21.621661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:23.573971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.918650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.728238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.454280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.781170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.553066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.183767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.038055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.772089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.420163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.390731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.032062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.677115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:21.791859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.003859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.042577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.842789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.587696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:05.900313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.661161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.295635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.167447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.883556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.537320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.499827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.144268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.790864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:21.923137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.194290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.161894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:27.955637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.700089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.021742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.774389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.409073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.283439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:12.990299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.668578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.604325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.253535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:19.905025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.030619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.453634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.281030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:28.067938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.824309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.151106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:07.889652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.523989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.398032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.104827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.782790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.718460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.373804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.025520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.150834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.590978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.402058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:28.182236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:03.959051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.274476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.009300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.641170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.577288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.218920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:14.898973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.847659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.494146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.144849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.321181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.719326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.527905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:28.285398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:04.076169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.436880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.116320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:09.904908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.697785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.324556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:15.004273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:16.954239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.600491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.260542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.432367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.836214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.641668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:28.419894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:04.212948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.599538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.246038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.042371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.839303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.458279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:15.136628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.086391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.734581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.490894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.576110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:24.999018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.783623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:28.545398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:04.341887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:06.728566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:08.376283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:10.170837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:11.974911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:13.594048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:15.263403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:17.211437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:18.863568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:20.647080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:22.735176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:25.138550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-19T20:25:26.915134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-19T20:25:35.508956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
IDageblackchascrimdisinduslstatmedvnoxptratioradrmtaxzn
ID1.0000.247-0.1150.2780.483-0.3900.3360.268-0.2650.4680.3050.598-0.0600.544-0.195
age0.2471.000-0.2240.0000.688-0.8200.6760.649-0.5550.7980.3660.402-0.3040.539-0.541
black-0.115-0.2241.0000.000-0.3440.245-0.295-0.1970.138-0.287-0.017-0.2720.055-0.2950.170
chas0.2780.0000.0001.0000.0000.0000.0540.0000.2120.1520.3160.1410.0000.0000.000
crim0.4830.688-0.3440.0001.000-0.7160.7160.628-0.5340.8100.4480.744-0.3660.734-0.562
dis-0.390-0.8200.2450.000-0.7161.000-0.747-0.5680.440-0.871-0.313-0.4540.331-0.5630.604
indus0.3360.676-0.2950.0540.716-0.7471.0000.641-0.5790.7760.4400.426-0.4780.663-0.636
lstat0.2680.649-0.1970.0000.628-0.5680.6411.000-0.8650.6380.4640.377-0.6460.536-0.472
medv-0.265-0.5550.1380.212-0.5340.440-0.579-0.8651.000-0.557-0.538-0.3070.635-0.5420.436
nox0.4680.798-0.2870.1520.810-0.8710.7760.638-0.5571.0000.3880.573-0.3620.660-0.619
ptratio0.3050.366-0.0170.3160.448-0.3130.4400.464-0.5380.3881.0000.311-0.3350.449-0.438
rad0.5980.402-0.2720.1410.744-0.4540.4260.377-0.3070.5730.3111.000-0.1370.718-0.263
rm-0.060-0.3040.0550.000-0.3660.331-0.478-0.6460.635-0.362-0.335-0.1371.000-0.3200.399
tax0.5440.539-0.2950.0000.734-0.5630.6630.536-0.5420.6600.4490.718-0.3201.000-0.372
zn-0.195-0.5410.1700.000-0.5620.604-0.636-0.4720.436-0.619-0.438-0.2630.399-0.3721.000

Missing values

2024-08-19T20:25:28.715906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-19T20:25:29.010736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDcrimzninduschasnoxrmagedisradtaxptratioblacklstatmedv
010.0063218.02.3100.5386.57565.24.0900129615.3396.904.9824.0
120.027310.07.0700.4696.42178.94.9671224217.8396.909.1421.6
240.032370.02.1800.4586.99845.86.0622322218.7394.632.9433.4
350.069050.02.1800.4587.14754.26.0622322218.7396.905.3336.2
470.0882912.57.8700.5246.01266.65.5605531115.2395.6012.4322.9
5110.2248912.57.8700.5246.37794.36.3467531115.2392.5220.4515.0
6120.1174712.57.8700.5246.00982.96.2267531115.2396.9013.2718.9
7130.0937812.57.8700.5245.88939.05.4509531115.2390.5015.7121.7
8140.629760.08.1400.5385.94961.84.7075430721.0396.908.2620.4
9150.637960.08.1400.5386.09684.54.4619430721.0380.0210.2618.2
IDcrimzninduschasnoxrmagedisradtaxptratioblacklstatmedv
3234910.207460.027.7400.6095.09398.01.8226471120.1318.4329.688.1
3244920.105740.027.7400.6095.98398.81.8681471120.1390.1118.0713.6
3254930.111320.027.7400.6095.98383.52.1099471120.1396.9013.3520.1
3264940.173310.09.6900.5855.70754.02.3817639119.2396.9012.0121.8
3274980.268380.09.6900.5855.79470.62.8927639119.2396.9014.1018.3
3285000.177830.09.6900.5855.56973.52.3999639119.2395.7715.1017.5
3295020.062630.011.9300.5736.59369.12.4786127321.0391.999.6722.4
3305030.045270.011.9300.5736.12076.72.2875127321.0396.909.0820.6
3315040.060760.011.9300.5736.97691.02.1675127321.0396.905.6423.9
3325060.047410.011.9300.5736.03080.82.5050127321.0396.907.8811.9